README.md

---
license: apache-2.0
datasets:
- sajaw/Arasquad3_llama2_version2
- sajaw/GQA_llama2_version
language:
- ar
metrics:
- bertscore
pipeline_tag: question-answering
---
# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

This model is an LoRA adapter file from finetuned Llama-2-7b-hf model. This is an experimental model.


To run it, you need to:

Agree with Meta's agreements to download the Llama-2-13b-chat-hf model from here: https://huggingface.co/meta-llama/Llama-2-13b-chat-hf
Clone this repository
Clone the Alpaca-LoRA repository from here: https://github.com/tloen/alpaca-lora
Use this command to run it: -python generate.py
--load_8bit
--base_model 'PATH_TO_YOUR_LOCAL_LLAMA_2_7B_CHAT_HF'
--lora_weights 'PATH_TO_YOUR_LOCAL_FILE_OF_THIS_MODEL'
You must agree with Meta/Llama-2's agreements to use this model.
## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->



- **Developed by:** Saja Nakhleh

- **Model type:** Question answering model
- **Language(s) (NLP):** Arabic
- **Finetuned from model [optional]:**  llama-2-7b
### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Paper [optional]:** Not Yet

## Bias, Risks, and Limitations

This model performs well with hetrogenius data.



### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

None
## How to Get Started with the Model

Use the code below to get started with the model.

from transformers import AutoConfig, AutoModel, AutoTokenizer
config = AutoConfig.from_pretrained("sajaw/llama-2-7b-RandomGPT-5K-ar")
model = AutoModel.from_pretrained("sajaw/llama-2-7b-RandomGPT-5K-ar")
tokenizer = AutoTokenizer.from_pretrained("sajaw/llama-2-7b-RandomGPT-5K-ar")

## Training Details

### Training Data

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

- sajaw/Arasquad3_llama2_version2
- sajaw/GQA_llama2_version


#### Preprocessing [optional]

Context, questions and answers are concatinated with the instructions in one "message" record


#### Training Hyperparameters

- **Training regime:** NA <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

NA

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Dataset Card if possible. -->

We have used 250 samples from AraSquad as true samples to test the model

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
NA

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

F1-score, precision, recall

### Results

F1-score= 0.6818	
precision= 0.6564	
recall=0.7226

#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

NA

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** kaggle - GPU T4 *2
- **Hours used:** 9 hours
- **Cloud Provider:** kaggle
- **Compute Region:** NA
- **Carbon Emitted:** NA

## Technical Specifications [optional]

### Model Architecture and Objective

NA

### Compute Infrastructure

NA

#### Hardware

NA

#### Software

NA

## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**


**APA:**



## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

NA

## More Information [optional]

NA

## Model Card Authors [optional]

Saja Nakhleh

## Model Card Contact

[email protected]